A few weeks ago, I wrote a post on this site about the data-driven approach to editing I took on 101 Lightbulb Moments in Data Management. Call me a geek (you wouldn’t be the first), but using tools like pivot tables and advanced filters in Excel made curating the book easier.
Now, if you’re reading this, you’re probably not an author, but I’ll bet that you’re at least vaguely interested in data management. So keep reading. There’s a payoff at the end of this post.
It turns out that other writers believe in the data-driven approach to publishing. Case in point: Eric Ries. Now a rock star, Ries didn’t take a traditional approach to publishing his first book, The Lean Startup. As he explains in the following interview, he conducted tests well before the book was ready for publication:
Many of his tests were about the cover and the subtitle – the two things he thought would be most impactful, and two things that are traditionally hard to analyze to determine the best of each. He describes how he fought with his publisher over “horrendous” covers that were presented to him. He was thrilled to have empirical data to show them, based on what people reacted most favorably to – what actually drove book sales. In the end, they tested and tested until he found something that he liked, the publisher liked, and actually sold books.
What’s more, he shared the data on his experiments, and used this too to sell books. One idea that really sold a lot of books for him was offering people to see this data if you pre-ordered a book. So Eric offered people a chance to go behind the scenes to see the book marketing testing that he was doing on the site. Due to the nature of the book, testing ideas that work, this really resonated with his audience. All the data can still be accessed on his website, including experiments and trends among his book-buying customers.
Two things strike me about Ries’ approach. First, it’s very much a Black Swan. That is, his approach may explain his success in hindsight, but that doesn’t mean that any aspiring author can now follow his formula and expect anywhere near the same results. What’s more, no one could have predicted it in advance.
Second, far too many people in the Establishment (in this case, traditional publishers) by default don’t even think about data most of the time. Only when Ries “proved” that the aforementioned “horrendous” covers deterred potential buyers did his publisher consider alternatives. (Lamentably, I know exactly he’s talking about.)
The results of Ries’ efforts are hard to understate. The book has become a best-seller and Ries has gained tremendous leverage with his publisher for future books. After all, he has broken through and become an A-list author.
To that end, learn from Ries’ strategy here. Collect and maintain our own data if you’re trying to convince your organization to do something. To be sure, naysayers and data skeptics will always pooh-pooh the importance of good, clean data. If confronted with obstinate, data-averse folks, go get your own data and prove them wrong.
What say you?